Metaphor Identification in Large Texts Corpora
Identifying metaphorical language-use (e.g., sweet child) is one of the challenges facing natural language processing. This paper describes three novel algorithms for automatic metaphor identification. The algorithms are variations of the same core algorithm. We evaluate the algorithms on two corpor...
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| Published in | PloS one Vol. 8; no. 4; p. e62343 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
29.04.2013
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0062343 |
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| Abstract | Identifying metaphorical language-use (e.g., sweet child) is one of the challenges facing natural language processing. This paper describes three novel algorithms for automatic metaphor identification. The algorithms are variations of the same core algorithm. We evaluate the algorithms on two corpora of Reuters and the New York Times articles. The paper presents the most comprehensive study of metaphor identification in terms of scope of metaphorical phrases and annotated corpora size. Algorithms' performance in identifying linguistic phrases as metaphorical or literal has been compared to human judgment. Overall, the algorithms outperform the state-of-the-art algorithm with 71% precision and 27% averaged improvement in prediction over the base-rate of metaphors in the corpus. |
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| AbstractList | Identifying metaphorical language-use (e.g., sweet child) is one of the challenges facing natural language processing. This paper describes three novel algorithms for automatic metaphor identification. The algorithms are variations of the same core algorithm. We evaluate the algorithms on two corpora of Reuters and the New York Times articles. The paper presents the most comprehensive study of metaphor identification in terms of scope of metaphorical phrases and annotated corpora size. Algorithms’ performance in identifying linguistic phrases as metaphorical or literal has been compared to human judgment. Overall, the algorithms outperform the state-of-the-art algorithm with 71% precision and 27% averaged improvement in prediction over the base-rate of metaphors in the corpus. Identifying metaphorical language-use (e.g., sweet child) is one of the challenges facing natural language processing. This paper describes three novel algorithms for automatic metaphor identification. The algorithms are variations of the same core algorithm. We evaluate the algorithms on two corpora of Reuters and the New York Times articles. The paper presents the most comprehensive study of metaphor identification in terms of scope of metaphorical phrases and annotated corpora size. Algorithms' performance in identifying linguistic phrases as metaphorical or literal has been compared to human judgment. Overall, the algorithms outperform the state-of-the-art algorithm with 71% precision and 27% averaged improvement in prediction over the base-rate of metaphors in the corpus.Identifying metaphorical language-use (e.g., sweet child) is one of the challenges facing natural language processing. This paper describes three novel algorithms for automatic metaphor identification. The algorithms are variations of the same core algorithm. We evaluate the algorithms on two corpora of Reuters and the New York Times articles. The paper presents the most comprehensive study of metaphor identification in terms of scope of metaphorical phrases and annotated corpora size. Algorithms' performance in identifying linguistic phrases as metaphorical or literal has been compared to human judgment. Overall, the algorithms outperform the state-of-the-art algorithm with 71% precision and 27% averaged improvement in prediction over the base-rate of metaphors in the corpus. |
| Author | Neuman, Yair Howard, Newton Last, Mark Argamon, Shlomo Frieder, Ophir Assaf, Dan Cohen, Yohai |
| AuthorAffiliation | 4 Department of Computer Science, Illinois Institute of Technology, Chicago, Illinois, United States of America 3 Department of Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel 6 Department of Computer Science, Georgetown University, Washington, D.C., United States of America 2 Gilasio Coding, Ltd., Tel-Aviv, Israel 1 Department of Education, Ben-Gurion University of the Negev, Beer-Sheva, Israel 5 Synthetic Intelligence Lab, Massachusetts Institute of Technology, Boston, Massachusetts, United States of America Max Planck Institute for the Physics of Complex Systems, Germany |
| AuthorAffiliation_xml | – name: 3 Department of Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel – name: 1 Department of Education, Ben-Gurion University of the Negev, Beer-Sheva, Israel – name: 2 Gilasio Coding, Ltd., Tel-Aviv, Israel – name: Max Planck Institute for the Physics of Complex Systems, Germany – name: 5 Synthetic Intelligence Lab, Massachusetts Institute of Technology, Boston, Massachusetts, United States of America – name: 6 Department of Computer Science, Georgetown University, Washington, D.C., United States of America – name: 4 Department of Computer Science, Illinois Institute of Technology, Chicago, Illinois, United States of America |
| Author_xml | – sequence: 1 givenname: Yair surname: Neuman fullname: Neuman, Yair – sequence: 2 givenname: Dan surname: Assaf fullname: Assaf, Dan – sequence: 3 givenname: Yohai surname: Cohen fullname: Cohen, Yohai – sequence: 4 givenname: Mark surname: Last fullname: Last, Mark – sequence: 5 givenname: Shlomo surname: Argamon fullname: Argamon, Shlomo – sequence: 6 givenname: Newton surname: Howard fullname: Howard, Newton – sequence: 7 givenname: Ophir surname: Frieder fullname: Frieder, Ophir |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23658625$$D View this record in MEDLINE/PubMed |
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| Copyright | 2013 Neuman et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2013 Neuman et al 2013 Neuman et al |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Designed the algorithms: YN DA YC ML. Conceived and designed the experiments: YN DA YC ML SA NH OF. Performed the experiments: YN DA YC. Analyzed the data: YN DA YC ML SA NH OF. Contributed reagents/materials/analysis tools: YN DA YC ML SA NH OF. Wrote the paper: YN DA YC ML SA NH OF. Competing Interests: YC is the owner of Gilasio Coding Ltd. There are no patents, products in development or marketed products to declare. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials. |
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| SubjectTerms | Algorithms Cognition & reasoning Comprehension Computer Science Dictionaries Humans Identification Language Linguistics Mathematics Medicine Metaphor Metaphors Natural language Natural Language Processing Neurosciences Random variables Semantic web Semantics Social and Behavioral Sciences Theory Word sense disambiguation |
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| Title | Metaphor Identification in Large Texts Corpora |
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